

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				:      Union[str, Any]   		=			logging.get_logger(__name__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						str 							= """huggingface/label-files"""
			a_						:						Optional[Any] 							= """imagenet-1k-id2label.json"""
			a_						:						Tuple 							= json.load(open(hf_hub_download(a__  ,	a__  ,	repo_type="""dataset""")  ,	"""r"""))
			a_						:						Tuple 							= {int(a__): v for k, v in idalabel.items()}
			a_						:						int 							= {v: k for k, v in idalabel.items()}

			a_						:						Optional[int] 							= """std_conv""" if """bit""" in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			a_						:						Dict 							= BitConfig(
			    conv_layer=a__  ,	num_labels=1_0_0_0  ,	idalabel=a__  ,	labelaid=a__  ,	)

			return config


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if "stem.conv" in name:
						a_						:						Dict 							= name.replace("""stem.conv"""  ,	"""bit.embedder.convolution""")
			if "blocks" in name:
						a_						:						Tuple 							= name.replace("""blocks"""  ,	"""layers""")
			if "head.fc" in name:
						a_						:						List[Any] 							= name.replace("""head.fc"""  ,	"""classifier.1""")
			if name.startswith("""norm"""):
						a_						:						int 							= """bit.""" + name
			if "bit" not in name and "classifier" not in name:
						a_						:						str 							= """bit.encoder.""" + name

			return name


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Optional[int] 							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a_						:						Union[str, Any] 							= Image.open(requests.get(a__  ,	stream=a__).raw)
			return im


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__=False):

			'''simple docstring'''



			a_						:						Any 							= get_config(a__)

			# load original model from timm
			a_						:						str 							= create_model(a__  ,	pretrained=a__)
			timm_model.eval()

			# load state_dict of original model
			a_						:						Tuple 							= timm_model.state_dict()
			for key in state_dict.copy().keys():
						a_						:						Optional[Any] 							= state_dict.pop(a__)
						a_						:						Dict 							= val.squeeze() if """head""" in key else val

			# load HuggingFace model
			a_						:						List[str] 							= BitForImageClassification(a__)
			model.eval()
			model.load_state_dict(a__)

			# create image processor
			a_						:						Optional[int] 							= create_transform(**resolve_data_config({}  ,	model=a__))
			a_						:						Any 							= transform.transforms

			a_						:						Dict 							= {
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			a_						:						Optional[Any] 							= BitImageProcessor(
			    do_resize=a__  ,	size={"""shortest_edge""": timm_transforms[0].size}  ,	resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,	do_center_crop=a__  ,	crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}  ,	do_normalize=a__  ,	image_mean=timm_transforms[-1].mean.tolist()  ,	image_std=timm_transforms[-1].std.tolist()  ,	)

			a_						:						int 							= prepare_img()
			a_						:						Optional[int] 							= transform(a__).unsqueeze(0)
			a_						:						Optional[int] 							= processor(a__  ,	return_tensors="""pt""").pixel_values

			# verify pixel values
			assert torch.allclose(a__  ,	a__)

			# verify logits
			with torch.no_grad():
						a_						:						Dict 							= model(a__)
						a_						:						str 							= outputs.logits

			print("""Logits:"""  ,	logits[0, :3])
			print("""Predicted class:"""  ,	model.config.idalabel[logits.argmax(-1).item()])
			a_						:						str 							= timm_model(a__)
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(a__  ,	outputs.logits  ,	atol=1e-3)
			print("""Looks ok!""")

			if pytorch_dump_folder_path is not None:
						Path(a__).mkdir(exist_ok=a__)
						print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}''')
						model.save_pretrained(a__)
						processor.save_pretrained(a__)

			if push_to_hub:
						print(f'''Pushing model {model_name} and processor to the hub''')
						model.push_to_hub(f'''ybelkada/{model_name}''')
						processor.push_to_hub(f'''ybelkada/{model_name}''')


if __name__ == "__main__":
	__snake_case				:      str   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""resnetv2_50x1_bitm""",
	    type=str,
	    help="""Name of the BiT timm model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	    help="""Whether to push the model to the hub.""",
	)

	__snake_case				:      Any   		=			parser.parse_args()
	convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





__snake_case				:      int   		=			[
    """Audio""",
    """Array2D""",
    """Array3D""",
    """Array4D""",
    """Array5D""",
    """ClassLabel""",
    """Features""",
    """Sequence""",
    """Value""",
    """Image""",
    """Translation""",
    """TranslationVariableLanguages""",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages


